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RationalBloks

by io.github.rationalbloks

Create Graph Node

create_graph_node

Create a single node in a deployed graph project.

REQUIRES: Project must be deployed (use deploy_graph_staging first).

The entity_type must match an entity key from the project schema. Use get_graph_data_schema to see available entity types and their fields.

Example: entity_type: "person" entity_id: "alan-turing-001" data: {"name": "Alan Turing", "birth_year": 1912, "field": "Computer Science"}

The entity_id is your unique identifier — use meaningful IDs for knowledge graphs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesNode properties matching the entity schema
entity_idYesUnique identifier for the node
project_idYesProject ID (UUID)
entity_typeYesEntity key (e.g., 'person', 'concept')
environmentNoEnvironment: staging or production (default: staging)

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate mutation (readOnlyHint=false) and non-destructiveness (destructiveHint=false). The description adds useful constraints: entity_type must match schema, and entity_id should be a meaningful unique identifier. It could further clarify the effect of the environment parameter or response behavior, but it does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured: a one-sentence summary, a required-prerequisite note, schema guidance, and an example. Every sentence earns its place, and the example is illustrative without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers prerequisites, schema matching, and unique ID guidance. However, it omits information about return values, error cases, or the environment parameter's default behavior, which would be useful given there is no output schema. Minor gap for a mutation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description adds value beyond the schema by explaining that entity_id is a unique identifier and providing a concrete example that illustrates how data should be structured for an entity type like 'person'. This clarifies the nested object usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Create') and resource ('single node in a deployed graph project'), clearly distinguishing it from sibling tools like bulk_create_graph_nodes and create_graph_relationship. It also states the key parameters (entity_type, entity_id, data) upfront.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states a required prerequisite: the project must be deployed, and directs users to deploy_graph_staging first. It also instructs users to consult get_graph_data_schema for valid entity types, providing clear when-to-use and preparation guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation4/5

Most tools are clearly differentiated by domain (project vs graph_project) and action (create, get, list, delete). The main ambiguity is get_project vs get_project_info, which both claim to return detailed project information. Otherwise tool boundaries are clear.

Naming Consistency4/5

The server follows a strong verb_noun convention, with parallel naming for graph and non-graph tools (create_project/create_graph_project, deploy_staging/deploy_graph_staging). Minor deviations include bulk_create_graph_nodes and fulltext_search_graph, but patterns remain predictable.

Tool Count2/5

48 tools is a heavy surface, even when accounting for the two parallel product domains (relational and graph). Many tools are near-duplicates across domains, and the count exceeds the 25-tool threshold that feels manageable. It would benefit from consolidation or sub-servers.

Completeness4/5

Both project types have full life-cycle coverage: create, schema management, deployment, rollback, and deletion, plus graph data operations including bulk, search, and traversal. Minor gaps exist, such as no update_graph_relationship and the redundant get_project/get_project_info pair, but agents can accomplish core workflows.

Resources